import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
_SCREAMING_SNAKE_CASE 		=      parser.parse_args()


_SCREAMING_SNAKE_CASE 		=      """cpu"""
_SCREAMING_SNAKE_CASE 		=      """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

_SCREAMING_SNAKE_CASE 		=      """path-to-your-trained-model"""
_SCREAMING_SNAKE_CASE 		=      StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
     _SCREAMING_SNAKE_CASE 		=      DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
_SCREAMING_SNAKE_CASE 		=      pipe.to(device)

# to channels last
_SCREAMING_SNAKE_CASE 		=      pipe.unet.to(memory_format=torch.channels_last)
_SCREAMING_SNAKE_CASE 		=      pipe.vae.to(memory_format=torch.channels_last)
_SCREAMING_SNAKE_CASE 		=      pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
     _SCREAMING_SNAKE_CASE 		=      pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
_SCREAMING_SNAKE_CASE 		=      torch.randn(2, 4, 6_4, 6_4)
_SCREAMING_SNAKE_CASE 		=      torch.rand(1) * 9_9_9
_SCREAMING_SNAKE_CASE 		=      torch.randn(2, 7_7, 7_6_8)
_SCREAMING_SNAKE_CASE 		=      (sample, timestep, encoder_hidden_status)
try:
     _SCREAMING_SNAKE_CASE 		=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
     _SCREAMING_SNAKE_CASE 		=      ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
_SCREAMING_SNAKE_CASE 		=      ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
_SCREAMING_SNAKE_CASE 		=      ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
     _SCREAMING_SNAKE_CASE 		=      ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
_SCREAMING_SNAKE_CASE 		=      6_6_6
_SCREAMING_SNAKE_CASE 		=      torch.Generator(device).manual_seed(seed)
_SCREAMING_SNAKE_CASE 		=      {"""generator""": generator}
if args.steps is not None:
     _SCREAMING_SNAKE_CASE 		=      args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
     _SCREAMING_SNAKE_CASE 		=      pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 if n == 0:
  return 0
 UpperCamelCase			  =      float("""-inf"""       )
 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max(
      UpperCamelCase_							,					prices[i - 1] + naive_cut_rod_recursive(n - i							,					UpperCamelCase_       )       )

 return max_revue





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 return _top_down_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	List[Any]:







 '''simple docstring'''
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  UpperCamelCase			  =      float("""-inf"""       )
  for i in range(1							,					n + 1       ):
   UpperCamelCase			  =      max(
       UpperCamelCase_							,					prices[i - 1] + _top_down_cut_rod_recursive(n - i							,					UpperCamelCase_							,					UpperCamelCase_       )							,					)

  UpperCamelCase			  =      max_revenue

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 _enforce_args(UpperCamelCase_							,					UpperCamelCase_       )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 UpperCamelCase			  =      [float("""-inf"""       ) for _ in range(n + 1       )]
 UpperCamelCase			  =      0

 for i in range(1							,					n + 1       ):
  UpperCamelCase			  =      max_rev[i]
  for j in range(1							,					i + 1       ):
   UpperCamelCase			  =      max(UpperCamelCase_							,					prices[j - 1] + max_rev[i - j]       )

  UpperCamelCase			  =      max_revenue_i

 return max_rev[n]





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_       )		->	Union[str, Any]:







 '''simple docstring'''
 if n < 0:
  UpperCamelCase			  =      f"""n must be greater than or equal to 0. Got n = {n}"""
  raise ValueError(UpperCamelCase_       )

 if n > len(UpperCamelCase_       ):
  UpperCamelCase			  =      (
      """Each integral piece of rod must have a corresponding price. """
      f"""Got n = {n} but length of prices = {len(UpperCamelCase_       )}"""
  )
  raise ValueError(UpperCamelCase_       )





def 			lowercase(	)		->	str:







 '''simple docstring'''
 UpperCamelCase			  =      [6, 10, 12, 15, 20, 23]
 UpperCamelCase			  =      len(UpperCamelCase_       )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 UpperCamelCase			  =      36

 UpperCamelCase			  =      top_down_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      bottom_up_cut_rod(UpperCamelCase_							,					UpperCamelCase_       )
 UpperCamelCase			  =      naive_cut_rod_recursive(UpperCamelCase_							,					UpperCamelCase_       )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
     main()
